A Siamese Network Combining Multiscale Joint Supervision and Improved Consistency Regularization for Weakly Supervised Building Change Detection

Abstract

Building change detection (BCD) from remote sensing images is essential in various practical applications. Recently, inspired by the achievement of deep learning in semantic segmentation (SS), methods that treat the BCD problem as a binary SS task using deep siamese networks have attracted increasing attention. However, similar to their counterparts, these approaches still face the challenge of collecting massive pixel-level annotations. To address this issue, this article presents a novel weakly supervised method for BCD from remote sensing images using image-level labels. The proposed method elaborately designs a siamese network to integrate a multiscale joint supervision (MJS) module and an improved consistency regularization (ICR) module into a unified framework to improve the so-called class activation maps (CAMs), which is vital for producing high-quality pseudomasks using imagelevel annotations to support pixel-level BCD. To be specific, the MSJ is used for generating refined multiscale CAMs to well capture changes at different scales corresponding to various buildings of varying sizes. The ICR contributes to improving the consistency of CAMs to highlight the boundaries of changed buildings. Extensive experiments on two public BCD datasets have demonstrated that the proposed method outperforms the current state-of-the-art approaches. Furthermore, the visual detection maps also indicate that the proposed method can achieve scale-adaptive change detection results and preserve object boundaries more effectively.

MSGFNet: Multi-Scale Gated Fusion Network for Remote Sensing Image Change Detection

“…In contrast to single-stream, two-stream methods leverage the Siamese architecture, which consists of two streams that share weights to generate features of bi-temporal images. Most existing CD methods [20,[29][30][31] adopt the Siamese architecture because it is appropriate for handling the input of RSIs. For instance, Dai et al [29] introduced a building CD method that comprises a multi-scale joint supervision module and an improved consistency regularization module.…”

“…Most existing CD methods [20,[29][30][31] adopt the Siamese architecture because it is appropriate for handling the input of RSIs. For instance, Dai et al [29] introduced a building CD method that comprises a multi-scale joint supervision module and an improved consistency regularization module. Ye et al [30] employed Siamese networks to propose a feature decomposition optimization reorganization network for CD.…”